Non-invasive method of determining skin thickness and characterizing layers of skin tissue in vivo

ABSTRACT

A novel approach to measuring the overall and layer-by-layer thickness of in vivo skin tissue based on near infrared absorbance spectra is described. The different biological and chemical compounds present in the various layers of a tissue sample have differing absorbance spectra and scattering properties that enable them to be discerned and quantified, thus allowing an estimate of the thickness of the tissue being sampled. The method of the invention also yields the chemical composition of the absorbing and/or scattering species of each layer. Additionally, a method of path length normalization for the purpose of noninvasive analyte prediction on the basis of skin thickness and layer constituents is provided.

[0001] This application is a Continuation of U.S. patent applicationSer. No. 09/514,039, filed Feb. 25, 2000.

BACKGROUND OF THE INVENTION

[0002] 1. Technical Field

[0003] The invention relates to the measurement of tissue properties.More particularly the invention relates to the measurement of skinthickness based on near-infrared absorbance spectra.

[0004] 2. Description of the Prior Art

[0005] Near infrared (NIR) tissue spectroscopy is a promisingnoninvasive technology that bases measurements on the irradiation of atissue site with NIR energy in the 700-2500 nm wavelength range. Theenergy is focused onto an area of the skin and propagates according tothe scattering and absorbance properties of the skin tissue. Thus,energy that is reflected by the skin or that is transmitted through theskin is detected provides information about the tissue volumeencountered. Specifically, the attenuation of the light energy at eachwavelength is a function of the structural properties and chemicalcomposition of the tissue. Tissue layers, each containing a uniqueheterogeneous particulate distribution, affect light absorbance throughscattering. Chemical components such as water, protein, fat and bloodanalytes absorb light proportionally to their concentration throughunique absorbance profiles or signatures. The measurement of tissueproperties, characteristics or composition is based on the technique ofdetecting the magnitude of light attenuation resulting from itsrespective scattering and/or absorbance properties.

[0006] Blood Analyte Prediction

[0007] While noninvasive prediction of blood analytes, such as bloodglucose concentration, has been pursued through NIR spectroscopy, thereported success and product viability has been limited by the lack of asystem for compensating for variations between individuals that producedramatic changes in the optical properties of the tissue sample. Forexample, see O. Khalil Spectroscopic and clinical aspects ofnon-invasive glucose measurement. Clin Chem vol. 45, pp. 165-77 (1999);or J. Roe, B. Smoller. Bloodless Glucose Measurements, Critical Reviewsin Therapeutic Drug Carrier Systems, vol. 15, no. 3, pp. 199-241,(1998). These variations are related to structural differences in theirradiated tissue sample between individuals and include, for example,the thickness of the dermis, distribution and density of skin collagenand percent body fat. While the absorbance features caused by structuralvariation are repeatable by subject, over a population of subjects theyproduce confounding nonlinear spectral variation. See C. Tan, B.Statham, R. Marks and P. Payne. Skin thickness measurement by pulsedultrasound: its reproducibility, validation and variability, BritishJournal of Dermatology, vol. 106, pp. 657-667, (1982). Also see S.Shuster, M. Black and E. McVitie, The influence of age and sex on skinthickness, skin collagen and density, British Journal of Dermatology,vol. 93, (1975). See also J. Durnin, and M. Rahaman, The assessment ofthe amount of fat in the human body from measurements of skin foldthickness, British Journal of Nutrition, vol. 21, (1967). Additionally,variations in the subject's physiological state affect the opticalproperties of tissue layers and compartments over a relatively shortperiod of time. Such variations, for example, may be related tohydration levels, changes in the volume fraction of blood in the tissue,hormonal stimulation, temperature fluctuations and blood hemoglobinlevels. The differences in skin thickness and the composition of thedifferent layers produce a confounding effect in the noninvasiveprediction of blood analytes.

[0008] While these structural and state variations are the largestsources of variation in the measured near-infrared absorbance spectra,they are not indicative of blood analyte concentrations. Instead, theycause significant nonlinear spectral variation that limits thenoninvasive measurement of blood analytes through optically basedmethods. For example, several reported methods of noninvasive glucosemeasurement develop calibration models that are specific to anindividual over a short period of time. See K. Hazen, Glucosedetermination in biological matrices using near-infrared spectroscopy,Doctoral Dissertation, University of Iowa, (August 1995). Also see M.Robinson, R. Eaton, D. Haaland, G. Koepp, E. Thomas, B. Stallard and P.Robinson, Noninvasive glucose monitoring in diabetic patients: apreliminary evaluation, Clin. Chem, vol. 38/9, pp. 1618-1622, (1992).Also see S. Malin, T. Ruchti, T. Blank, S. Thennadil and S. Monfre,Noninvasive prediction of glucose by near-infrared diffuse reflectancespectroscopy, Clin. Chem, vol. 45:9, pp. 1651-1658, (1999).

[0009] A related application, S. Malin, T. Ruchti, An Intelligent SystemFor Noninvasive Blood Analyte Prediction, U.S. patent application Ser.No 09/359,191; filed Jul. 22, 1999, disclosed an apparatus and procedurefor substantially reducing this problem by classifying subjectsaccording to spectral features that are related to the tissuecharacteristics prior to blood analyte prediction. The extractedfeatures are representative of the actual tissue volume irradiated. Thegroups or classes are defined on the basis of tissue similarity suchthat the spectral variation within a class is small compared to thevariation between classes. These internally consistent classes are moresuitable for multivariate analysis of blood analytes since the largestsource of spectral interference is substantially reduced. In thismanner, by grouping individuals according to the similarity of spectralcharacteristics that represents the tissue state and structure, theconfounding nonlinear variation described above is reduced andprediction of blood analytes is made more accurate.

[0010] The general method of classification relies on the determinationof spectral features most indicative of the sampled tissue volume. Themagnitude of such features represents an underlying variable, such asthe thickness of tissue or level of hydration. It would therefore behighly advantageous to have a non-invasive method of determining skinthickness and characterizing the chemical and structural properties ofthe various layers.

[0011] Skin Thickness Determination

[0012] Skin thickness determinations are valuable for several purposes.The thickness of skin tissue and the individual layers provide valuablediagnostic information in a number of circumstances. For example, skinthickness is an important indicator of changes in the skin due tochronological ageing and photo ageing. Skin thickness measurements alsoprovide important information related to a variety of endocrinedisorders. Furthermore, a relationship between skin thickness and bonedensity has been observed. Therefore, skin thickness measurements havepotential application in the diagnosis and monitoring of bone lossdisorders.

[0013] As discussed above, the skin thickness measurement providesinformation about one of the primary sources of tissue variability andis therefore effective for establishing the general category of thetissue structure. The various categories are suitable for furtherspectral analysis and calibrations such as blood analyte measurement.Finally, the thickness can be used in conjunction with a diffusereflectance spectrum for the purpose of path length normalization inspectroscopic examination of the skin.

[0014] The most common method of determining the thickness of the skinand its constituent layers is through histological examination of abiopsy specimen. Biopsy has the obvious disadvantage of being aninvasive procedure. The subjects must endure an appreciable level ofinconvenience and discomfort, and they are exposed to the risksassociated with any surgical procedure. It is also a time-consuming,multi-step procedure, requiring skilled medical personnel and multiplepieces of equipment. The ensuing histological examination requiresspecialized equipment and personnel trained in special laboratorytechniques such as tissue sectioning. A simple, non-invasive method ofdetermining skin thickness in vivo would be highly useful.

[0015] In fact, a non-invasive method of skin thickness determinationusing ultrasonography is known [see Tan, et al.]. A beam of ultrasoundis directed toward a target site. The reflected ultrasound is detectedand an image, or sonogram, of the site is generated. Subsequent visualinspection of the resulting image allows an estimation of overall skinthickness. While this method circumvents the obvious disadvantages ofbiopsy and histological examination, its utility is limited to providinga macroscopic image of the targeted tissue, reflecting the state of thetissue at the time of examination. Ultrasonography cannot providedetailed information concerning the individual tissue layers. It wouldbe desirable to have a quantitative method of skin thicknessdetermination that also allowed the structural and chemicalcharacterization of the individual layers that the skin comprises, andthat provided data for further analysis and classification, such asblood analyte prediction.

SUMMARY OF THE INVENTION

[0016] Disclosed is a novel approach to measuring the overall andlayer-by-layer thickness of in vivo skin tissue based on near infraredabsorbance spectra. The disclosed methods also yield the chemicalcomposition of the absorbing and/or scattering species of each layer.Finally, a method of path length normalization for the purpose ofnoninvasive analyte prediction on the basis of skin thickness and layerconstituents is disclosed.

[0017] All procedures are based on the measurement of the absorbance ofnear-infrared light at a target tissue site. Near infrared measurementsare made either in transmission or diffuse reflectance using commonlyavailable NIR spectrometers, or by means of an LED array, to produce aspectrum of absorbance values. The method of skin thickness measurementrelies on the fact that different biological and chemical compounds havediffering absorbance spectra and scattering characteristics that can bediscerned and quantified accordingly. These and other features, aspectsand advantages of the invention will be better understood with referenceto the following description, drawings and appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018]FIG. 1 is a block diagram of a general procedure for determiningthe magnitude of target analytes and the skin thickness of target layersaccording to the invention;

[0019]FIG. 2 shows relative magnitudes of water and trigylcerides for 10subjects, plotted by sex according to the invention; and

[0020]FIG. 3 shows a plot of estimated skin fold thickness versus actualskin fold thickness for nineteen subjects according to the invention.

DETAILED DESCRIPTION

[0021] The invention provides two general methods of skin thicknessprediction on the basis of near-IR (NIR) spectral measurements. Thefirst method also yields information relating to the structure andcomposition of the absorbing and scattering species in each layer.Further, knowledge of the thickness and optical properties of theindividual tissue layers can be applied in a method of pathlengthnormalization to minimize the interference due to the variation of theindividual layers.

[0022] Method 1—Determination of Skin Thickness on the Basis of MarkerConstituents

[0023] The primary method takes advantage of the presence of keyindicators. Key indicators are the chemical or structural componentsthat are primary absorbers and/or scatterers in each particular tissuelayer, and that are not present in significant amounts (spectrally) inother layers. This allows for the exploitation of distinct spectralcharacteristics and features that are specific to certain tissueregions, or layers, based solely on such spectral measurements. Thespectral manifestation of these key indicators makes it possible toquantify the primary constituents and to determine the thickness of theindividual tissue layers.

[0024] The key indicators are determined from a priori knowledge of thecomposition and structure of skin tissue layers. Examples of keyindicators are provided in the table below: Key Indicator Tissue Regionof Significance Triglycerides Subcutaneous Tissue Collagen BundlesDermis Water Dermis Blood Dermis Keratinocytes Epidermis Lipids (FattyAcids) Epidermis Lipids, Specialized Epidermis (Sterols, Sphingolipids)Pigments Epidermis Corneocytes, Keratinized Cells Stratum Corneum SebumStratum Corneum

[0025] For example, because water is present in the dermis in greaterconcentration than in the epidermis or subcutaneous tissue, water isspecified as a key indicator for the dermis. Similarly, because highconcentrations of triglycerides are found primarily in adipose tissuewith relatively little found in the epidermis or dermis, trigylceridesare specified as a key indicator for adipose tissue, also known assubcutaneous tissue. Collagen bundles can be used as an additional keyindicator for the dermis. The epidermis can be discriminated by thescattering and/or absorbance of keratinocytes, while the stratum corneumis distinguished by the scattering and absorbance of corneocytes,keratinized cells, and specialized lipids.

[0026] The procedure for measuring the magnitude of the key indicatorsand skin thickness is shown in FIG. 1. First, a library of normalizedNIR absorbance spectra 10 of the key indicators is provided. The spectra10 of the key indicators are stored in the memory of a computerassociated with a spectrometer device. A suitable system for executingthe procedures and methods disclosed herein is described in a relatedapplication to the current application, T. Ruchti, S. Malin AnIntelligent System for Noninvasive Blood Analyte Prediction, U.S. patentapplication Ser. No. 09/359,191, filed Jul. 22, 1999. A NIR absorbancemeasurement 11 of the targeted tissue site is made in the wavelengthregion(s) in which both the key indicators specific to the target layerabsorb or scatter and in which light penetration to the target tissuelayer is optimal. The normalized pure component spectra of the keyindicators are projected 12 onto the measured absorbance spectrum.Alternately, the spectra of the key indicators are used as a basis setand the method of partial least squares is used to determine the optimalmagnitude of each to represent the measured absorbance spectrum.

[0027] The calculated magnitude 13 of each normalized key indicatorprovides a relative concentration of its respective constituent in thetissue. A composition calibration model 14 is applied to the calculatedmagnitudes to determine the actual concentration 15 of the constituent.In the related application cited above Ser. No. 09/359,191, a detaileddescription of a procedure for calculating such a calibration model isgiven.

[0028] Alternatively, the relative concentrations of the key indicatorsare processed by an alternate calibration model 16 for estimating skinthickness to determine the thickness of the target layer 17. It will beapparent to one skilled in the art that since key indicators arespecific to a given layer, their relative absorbances are directlyrelated to the thickness of the targeted layer(s). One skilled in theart will further appreciate that an overall thickness estimate may bearrived at by a simple summing of the thickness estimates of theindividual layers.

[0029] The skin thickness calibration model 16 is calculated from acalibration set (not shown) of exemplary measurements that provides boththe relative concentrations of the key indicators, calculated fromabsorbance spectra, and the thickness of each tissue layer. Thecalibration model is determined through multiple linear regression,partial least squares regression, artificial neural networks or othertechniques such that the thickness of each layer is predicted through amathematical mapping of the relative magnitude of the markerconstituents. The related application Ser. No. 09/359,191, previouslyreferred to, provides a detailed description of a procedure forcalculating the skin thickness calibration model 16 heretoforedescribed.

[0030] Two alternative experimental methods for realizing thecalibration set are provided below. In the first method, spectralmeasurements of a target area of human skin are obtained using a NIRreflectance instrument. Biopsies of the scanned region are then obtainedand examined histologically. The thickness and chemical composition ofthe key indicators specific to each tissue layer were included in thecalibration set. Using multivariate regression analysis techniques, acalibration model can then be developed to relate the spectral skinmeasurements, known as predictor variables, to the known skin layerthickness and chemical compositions, known as response variables. Thistechnique uses a priori information regarding the general physiology ofskin and exploits the inherent difference between skin layers and theircompositions to develop a model that predicts skin layer thickness andcomposition noninvasively.

[0031] The second approach is to develop a tissue model that adequatelyrepresents the fundamental absorbing and scattering characteristics ofan in vivo tissue system. Although, living tissue is a highly complexsystem, the transform from an in vivo system to a tissue model is madepossible by an a priori knowledge of the primary absorbing andscattering species present in the living tissue system. Becasue themodel also includes a known thickness for each tissue layer, and sincethe concentrations of absorbing and scattering components are known, aMonte Carlo simulation may be used to simulate the photon propagation oflight through the tissue model. The result of the Monte Carlo simulationis a diffuse reflectance measurement that is comparable to an actualreflectance measurement obtained experimentally. The tissue model mustbe validated in order to confirm that the model mirrors the complexityof the living tissue with sufficient accuracy to produce analogousresults in application.

[0032] Experimental Results

[0033] A study was performed using ten subjects, five males and fivefemales. NIR absorbance spectra were collected using a customspectrometer in diffuse reflectance mode. The pure component absorbancespectrum of water and fat were projected onto the measured spectrum inthe 1100-1400 nm range and the resulting magnitudes are plotted, by sex,in FIG. 2. The figure shows a systematic difference in the relativemagnitudes of the key indicators by sex. The subjects assorted into twodistinct groups, with the males tending to exhibit high magnitudes ofwater absorbance, indicating a relatively thicker dermis, and lowmagnitudes of trigylceride absorbance, indicating a relatively thinnersubcutaneous or adipose layer. Conversely, the females tended to exhibitlow magnitudes for water absorbance and high magnitudes for trigylcerideabsorbance, suggesting a relatively thinner dermis and a relativelythicker subcutaneous or adipose layer. Such a systematic difference isconsistent with that reported in the literature, i.e. a thicker layer ofadipose tissue in females than in males and a thinner dermis in femalesthan males [see Tan, et al., op.cit.]. Thus, the gross measurement ofrelative skin thickness through an NIR diffuse reflectance measurementis amply demonstrated. Quantification of the measurement is accomplishedthrough calibrations based on prior in vivo measurements or Monte Carlosimulations as described above.

[0034] Method 2—Skin Thickness on the Basis of a General CalibrationModel

[0035] The second method employs a general calibration model to predictthe total skin thickness or the thickness of target layers on the basisof the measured absorbance spectrum. In overview, the method includesthe following steps:

[0036] providing a calibration set of exemplary measurements;

[0037] measuring the NIR spectrum of a target layer at a tissue site;

[0038] processing the NIR spectral measurement through a generalcalibration model; and

[0039] arriving at an thickness estimate of the targeted tissue layer.

[0040] As previously described, an estimate of total thickness isderived by summing the thickness estimates for the individual tissuelayers. The general calibration model is based on a calibration set thatincludes spectral measurements, as previously described, made at atarget tissue measurement site on a diverse group of individuals, andthickness measurements of the individual layers based on histologicalanalysis of biopsy results or another commonly accepted method of skinthickness determination, pulsed ultrasound for example. The calibrationmodel is developed using known methods, including principal componentregression partial least squares regression and artificial neuralnetwork (see H. Martens, T. Naes. Multivariate Calibration, New York:John Wiley and Sons, (1989); P. Geladi, B. Kowalski, Partialleast-squares regression: a tutorial, Analytica Chimica Acta, vol. 185,pp. 1-17, (1986)). New absorbance spectra are then processed through thecalibration model to arrive at an estimate of skin thickness for thecorresponding tissue sample.

[0041] Experimental Results

[0042] A study was performed involving nineteen volunteers of diverseage (21-55 years) and sex (sixteen males and three females). Skin foldthickness of each participant was measured on the forearm with researchgrade calipers of the type known as HARPENDEN, manufactured by BritishIndicators, LTD. NIR scans of each subject were taken on the forearm anda calibration model for predicting the skin fold thickness was developedusing partial least squares regression. The model was evaluated throughcross-validation and the results are shown in FIG. 3. Estimated versusactual skinfold thickness determination were plotted for each subject.The standard error of prediction was 1.42, yielding a predictionaccuracy of 70 percent. The results clearly demonstrate the feasibilityof determining the thickness of a target layer from a generalcalibration model.

[0043] Pathlength Normalization

[0044] The differences in skin thickness and the composition of thedifferent layers produce a confounding effect in the noninvasiveprediction of blood analytes. In one individual, at a particular time,an absorbance spectrum is representative of a distinct tissue volumethat is sampled by the penetration of the light. When the target analytefor prediction is present in a particular layer it absorbs the light ina manner that is determined by its concentration and the pathlength oflight within the particular layer. However, this pathlength is afunction of the optical properties of the layer and the opticalproperties of the surrounding layers. Therefore, knowledge of thethickness of individual skin layers and their optical properties can beused to reduce the interference resulting from this nonlinear variation.

[0045] The skin thickness can be used in a classification system thatdevelops calibrations specific to groups or classes of individuals basedon tissue structure and state, fully described by Malin, et al. in thepreviously cited related application Ser. No. 09/359,191. However, in analternative method for reducing interference due to non-linearvariation, skin thickness and composition can be used with a nonlinearfunction to normalize the measured spectrum. The function can bedetermined from the light distributions in Monte Carlo simulationsinvolving skin models of diverse composition and thickness.

[0046] Although the invention has been described herein with referenceto certain preferred embodiments, one skilled in the art will readilyappreciate that other applications may be substituted for those setforth herein without departing from the spirit and scope of the presentinvention. Accordingly, the invention should only be limited by theclaims included below.

1. A non-invasive method of estimating thickness of in vivo skin tissueand characterizing constituents of tissue layers, comprising the stepsof: measuring a NIR absorbance spectrum of a target layer at a tissuesample site; processing said measured NIR absorbance spectrum to enhancesignal of a plurality of key indicators; calculating magnitude orrelative magnitude of at least one of said key indicators in saidmeasured spectrum; and applying at least one calibration model to saidcalculated magnitude or relative magnitude to characterize said tissuelayers.
 2. The method of claim 1, wherein said step of processing saidmeasured NIR absorbance spectrum comprises: projecting normalizedspectra of said key indicators on said measured spectrum.
 3. The methodof claim 1, wherein said key indicators comprise chemical and structuralcomponents that are primary absorbers and scatterers within a particulartissue layer, and wherein said magnitude of said key indicators isgreater in said particular layer of said tissue sample than in any otherlayer of said tissue sample, such that said magnitude of said keyindicators is specific to said particular tissue layer, and whereby saidparticular tissue layer can be characterized according to saidmagnitudes of said key indicators.
 4. The method of claim 3, whereintissue layers that can be characterized by calculating said magnitudesof said key indicators include any of: subcutaneous tissue; dermis;epidermis; and stratum corneum.
 5. The method of claim 3, wherein saidkey indicators are determined from a priori knowledge of the compositionand structure of said tissue layers, and wherein structural and chemicalcomponents that can serve as said key indicators include any of:trigylcerides; collagen bundles; water; blood; keratinocytes; fattyacids; sterols; sphingolipids; pigments; corneocytes; keratinized cells;and sebum.
 6. The method of claim 3, wherein said measuring stepcomprises the steps of: selecting a target tissue layer; selecting atleast one target key indicator specific to said target tissue layer; andlimiting said spectrum to a wavelength region wherein said at least onetarget key indicator absorbs and scatters, and wherein optimalpenetration of transmitted energy to said target layer is possible. 7.The method of claim 3, wherein said plurality of key indicatorscomprises a basis set.
 8. The method of claim 3, wherein saidcalculation step comprises the step of: applying a partial least squaresregression to calculate said magnitudes.
 9. The method of claim 3,wherein said calculated magnitudes of said key indicators providerelative concentrations of said structural and chemical components. 10.The method of claim 3, further comprising the step of: providing acalibration set of exemplary measurements.
 11. The method of claim 10,wherein said step of applying at least one calibration model comprisesthe step of: applying at least one calibration model to said relativeconcentrations to determine an actual concentration in said targetlayer, wherein said calibration model is calculated from saidcalibration set.
 12. The method of claim 10, wherein said step ofapplying at least one calibration model comprises the step of: applyingsaid calibration model to said relative concentrations to determineabsolute or relative thickness of said target layer, wherein thicknessis any of physical and optical thickness; and wherein said calibrationmodel is calculated from said calibration set.
 13. The method of claim10, wherein said exemplary measurements comprise calculated relativeconcentrations of said chemical and structural components and tissuelayer thickness determinations.
 14. The method of claim 13, wherein saidcalibration model is calculated using any of multiple linear regression,partial least squares regression, and artificial neural networks. 15.The method of claim 10, wherein said calibration set comprises NIRspectral measurements of an exemplary sample of skin tissue, tissuelayer thickness measurements determined from biopsies of said exemplarysample, and determinations of chemical composition of said layers ofsaid biopsy samples.
 16. The method of claim 15, wherein multivariateregression analysis relates said NIR spectral measurements of saidexemplary tissue sample to said layer thickness and chemical compositiondeterminations from said biopsy samples.
 17. The method of claim 10,wherein said calibration set comprises a tissue model that representsthe fundamental absorbing and scattering characteristics of an in vivotissue system.
 18. The method of claim 17, wherein said tissue modelemploys a simulation method whereby photon propagation of light throughsaid tissue model is simulated, and wherein said photon propagationsimulation yields a simulated diffuse reflectance spectrum comparable toan actual reflectance spectrum.
 19. The method of claim 18, wherein saidsimulation method is a Monte Carlo simulation.
 20. The method of claim12, further comprising the step of: summing thickness estimates
 21. Anon-invasive method of estimating thickness of in vivo skin tissuecomprising the steps of: measuring an NIR absorbance spectrum of atarget layer at a tissue sample site; applying at least one calibrationmodel to said absorbance spectrum; and determining a thickness estimateof said target layer of said tissue sample.
 22. The method of claim 21,further comprising the step of: providing a calibration set of exemplarymeasurements.
 23. The method of claim 20, wherein said calibration setcomprises spectral measurements of a target tissue site and tissue layerthickness determinations from an exemplary population of subjects. 24.The method of claim 23, wherein multivariate regression analysis relatessaid exemplary spectral measurements to said exemplary tissue layerthickness determinations.
 25. The method of claim 22, wherein saidcalibration model is calculated from said calibration set using any ofmultiple linear regression, partial least squares regression, andartificial neural networks.
 26. The method of claim 21, furthercomprising the step of: summing thickness estimates of individual targetlayers so that a total thickness of said tissue sample is calculated.